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Abstract

One of the major genomics challenges is to better understand how correct gene expression
is orchestrated. Recent studies have shown how spatial chromatin organization is critical
in the regulation of gene expression. Here, we developed a suite of computer programs
to identify chromatin conformation signatures with 5C technology http://Dostielab.biochem.mcgill.cawebcite. We identified dynamic HoxA cluster chromatin conformation signatures associated with cellular differentiation.
Genome-wide chromatin conformation signature identification might uniquely identify
disease-associated states and represent an entirely novel class of human disease biomarkers.

Rationale

Cell specialization is the defining hallmark of metazoans and results from differentiation
of precursor cells. Differentiation is characterized by growth arrest of proliferating
cells followed by expression of specific phenotypic traits. This process is essential
throughout development and for adult tissue maintenance. For example, improper cellular
differentiation in adult tissues can lead to human diseases such as leukemia [1,2]. For this reason, identifying mechanisms involved in differentiation is not only
essential to understand biology, but also to develop effective strategies for prevention,
diagnosis and treatment of cancer. Suzuki et al. recently defined the underlying transcription network of differentiation in the
THP-1 leukemia cell line [3]. Using several powerful genomics approaches, this study challenges the traditional
views that transcriptional activators acting as master regulators mediate differentiation.
Instead, differentiation is shown to require the concerted up- and down-regulation
of numerous transcription factors. This study provides the first integrated picture
of the interplay between transcription factors, proximal promoter activity, and RNA
transcripts required for differentiation of human leukemia cells.

Although extremely powerful, several observations indicate that implementation of
new technologies will be required to gain a full appreciation of how cells differentiate.
First, gene expression is controlled by a complex array of regulatory DNA elements.
Each gene may be controlled by multiple elements and each element may control multiple
genes [4]. Second, the functional organization of genes and elements is not linear along chromosomes.
For example, a given element may regulate distant genes or genes located on other
chromosomes without affecting the ones adjacent to it [4,5]. Third, gene regulation is known to involve both local and long-range chromatin structure
changes [6,7]. Although the role of histone and DNA modifications is increasingly well described,
relatively little is known about the function of spatial chromatin organization in
the regulation of genes. Interestingly, recent studies show that control DNA elements
can mediate long-range cis or trans regulation by physically interacting with target genes [8-10]. These studies indicate that genomes are organized into dynamic three-dimensional
networks of physical DNA contacts essential for proper gene expression (Figure 1a). Therefore, mapping the functional (physical) connectivity of genomes is essential
to fully identify the mechanisms involved in differentiation, and might provide important
diagnostic and prognostic signatures of human diseases.

Physical contacts between DNA segments can be measured with the 'chromosome conformation
capture' (3C) technologies [11,12]. The 3C approach (Figure 1b) uses formaldehyde to covalently link chromatin segments in vivo. Cross-linked chromatin is then digested with a restriction enzyme and ligated under
conditions promoting intermolecular ligation of cross-linked segments. Cross-links
are finally reversed by proteinase K digestion and DNA extraction to generate a '3C
library'. 3C libraries contain pair-wise ligation products, where the amount of each
product is inversely proportional to the original three-dimensional distance separating
these regions. These libraries are conventionally analyzed by semi-quantitative PCR
amplification of individual 'head-to-head' ligation junctions and agarose gel detection
(for details, see [12]). 3C was first used to show that long-range interactions are essential for gene expression
in several important mammalian genomic domains. For example, it was demonstrated that
the locus control region of the beta-globin locus specifically interacts with actively
transcribed genes but not with silent genes [13-16]. These contacts were required for gene expression and mediated by the hematopoietic
transcription factors GATA-1 and co-factor FOG-1 [15].

3C technology has been widely adopted for small-scale analysis of chromatin organization
at high-resolution [17-24]. However, this approach is technically tedious and not convenient for large-scale
studies. Genome-scale conformation studies can be performed quantitatively using the
3C-carbon copy (5C) technology (Figure 1c) [16,25]. The 5C approach combines 3C with the highly multiplexed ligation-mediated-amplification
technique to simultaneously detect up to millions of 3C ligation junctions. During
5C, multiple 5C primers corresponding to predicted 'head-to-head' 3C junctions are
first annealed in a multiplex setting to a 3C library. Annealed primers are then ligated
onto 3C contacts to generate a '5C library'. Resulting libraries contain 5C products
corresponding to 3C junctions where the amount of each product is proportional to
their original abundance in 3C libraries. 5C libraries are finally amplified by PCR
in a single step with universal primers corresponding to common 5C primer tails. These
libraries can be analyzed on custom microarrays or by high-throughput DNA sequencing
[16]. Although 5C technology is an ideal discovery tool and particularly well suited to
map functional interaction networks, this approach is not yet widely adopted partly
due to the lack of available resources.

In this study, we used the THP-1 leukemia differentiation system characterized by
Suzuki et al. [3] to identify chromatin conformation signatures (CCSs) associated with the transcription
network of cellular differentiation. To this end, we mapped physical interaction networks
with the 3C/5C technologies in the transcriptionally regulated HoxA cluster and in a silent gene desert region. The HoxA genes were selected for their pivotal roles in human biology and health. Importantly,
the HoxA cluster encodes 2 oncogenes, HoxA9 and HoxA10, which are over expressed in THP-1 cells. This genomic region plays an important
role in promoting cellular proliferation of leukemia cells and HoxA CCS identification should, therefore, help understand the mechanisms involved in regulating
these genes.

Using 3C, we found that repression of HoxA9, 10, 11 and 13 expression is associated with formation of distinct contacts between the genes and
with an overall increase in chromatin packaging. Chromatin remodeling was specific
to transcriptionally regulated domains since no changes were observed in the gene
desert region. We developed a suite of computer programs to assist in 5C experimental
design and data analysis and for spatial modeling of 5C results. We used these tools
to generate large-scale, high-resolution maps of both genomic regions during differentiation.
5C analysis recapitulated 3C results and identified new chromatin interactions involving
the transcriptionally regulated HoxA region. Three-dimensional modeling provided the first predicted conformations of a
transcriptionally active and repressed HoxA gene cluster based on 5C data. Importantly, these models identify CCSs of human leukemia,
which may represent an entirely novel class of human disease biomarker. 5C research
tools are now publicly available on our 5C resource website (see Materials and methods).

Results and discussion

We mapped physical interaction networks of the HoxA cluster and of a control gene desert region in the THP-1 differentiation system characterized
by Suzuki et al. [3]. THP-1 are myelomonocytic cells derived from an infant male with acute myeloid leukemia.
These cells terminally differentiate into mature monocytes/macrophages following stimulation
with phorbol myristate acetate (PMA; Figure 2a) [26-28]. THP-1 cells express the MLL-AF9 fusion oncogene originating from the translocation t(9;11)(p22;q23) between the mixed-lineage
leukemia (MLL) and AF9 genes [29,30]. MLL gene rearrangements are frequently found in both therapy-related and infantile
leukemia, and promote cellular proliferation by inducing aberrant expression of oncogenes,
including HoxA9 and A10 [31-35].

Figure 2. 5' end HoxA genes are repressed during cellular differentiation. (a) Cellular differentiation system used in this study. The human myelomonocytic cell
line THP1 was stimulated with PMA to cease proliferation and induce differentiation
into mature monocytes/macrophages. (b) Linear schematic representation of the human HoxA gene cluster on chromosome 7. Genes are represented by left facing arrows to indicate
direction of transcription. Cluster is presented in a 3' (HoxA1) to 5' (HoxA13) orientation. Same family members are labeled with identical color. Paralogue groups
(1-13) are identified above each gene. (c) Quantitative real-time PCR analysis of HoxA genes during cellular differentiation. Steady-state mRNA levels in undifferentiated
(left) and differentiated cells (right) were normalized relative to actin. CD14 and
ApoE expression levels were measured to verify cellular differentiation. Number below
each histogram bar identifies paralogue group. Asterisks indicate mRNA expression
below quantitative real-time PCR detection levels. Each histogram value is the average
of at least three PCRs and error bars represent the standard deviation.

Hox genes encode transcription factors of the homeobox superfamily [36]. In mammals, there are 39 Hox genes organized into 4 genomic clusters of 13 paralogue groups. The HoxA, B, C, and D clusters are each located on different chromosomes. For example, the HoxA cluster is located on human chromosome 7 and encodes 11 evolutionarily conserved genes
(Figure 2b). Undifferentiated THP-1 cells are known to express high levels of 5' end HoxA genes, which are repressed following PMA-induced differentiation [3]. We first verified that HoxA genes were regulated in our samples by measuring steady-state mRNA levels with quantitative
real-time PCR (Figure 2c). As expected, we found that HoxA9, 10, 11 and 13 were highly expressed in undifferentiated THP-1 compared to the other paralogues (Figure
2c, left). Expression of these genes was significantly reduced following differentiation
(Figure 2c, right), whereas the macrophage-specific ApoE and CD14 markers were induced in mature
monocytes/macrophages. These results indicate that HoxA genes are correctly regulated under our experimental conditions. RT-PCR primer sequences
used in this analysis are presented in Additional data file 1.

Hox genes are master regulators of development and play pivotal roles during adult tissue
differentiation. During development, the expression of Hox genes is regulated both spatially and temporally in an order that is colinear with
their organization along chromosomes [37-39]. This colinearity has fascinated biologists for over 25 years and strongly suggests
that chromatin structure plays an important role in their regulation. We first used
the conventional 3C method to determine whether HoxA gene regulation is accompanied by changes in spatial chromatin architecture. 3C libraries
from undifferentiated and differentiated THP-1 cells, and a control library prepared
from bacterial artificial chromosome (BAC) clones were generated as described in Materials
and methods. These libraries were used to characterize chromatin contacts within the
transcriptionally regulated 5' end HoxA region (Figure 3a, b, top). In undifferentiated cells, the HoxA9 promoter region was found to interact frequently with neighboring fragments ('Fixed
HoxA9' in Figure 3a). Additionally, the interaction frequency (IF) did not rapidly decrease with increasing
genomic distance. In contrast, HoxA9 repression in differentiated cells was accompanied by formation of very strong looping
contacts and by overall increased interaction frequency. Interestingly, looping fragments
contained other down-regulated genes, suggesting that HoxA repression involves increased chromatin packaging mediated by the specific clustering
of co-regulated genes.

Figure 3. Extensive spatial chromatin remodeling accompanies 5' HoxA gene repression during cellular differentiation. (a) Conventional 3C analysis of transcriptionally regulated HoxA genes. Chromatin contacts between the HoxA9, A10, A11, or A13 genes and surrounding genomic domain were measured in undifferentiated and differentiated
cells. The y-axis indicates normalized interaction frequency; the x-axis shows genomic
position relative to start of domain characterized. The genomic domain is shown to
scale above the graphs, and is as described in Figure 2b. Solid orange vertical lines
identify the position of the 'fixed' 3C region analyzed in each graph. Shaded green
vertical lines highlight the position of putative DNA looping contacts. Each data
point is the average of at least three PCRs. Error bars represent the standard error
of the mean. (b) Chromatin contact changes during cellular differentiation. 3C interactions between
the HoxA9, A10, A11, or A13 genes and surrounding genomic domain presented in (a) were compared in both cellular
states by calculating fold differences (log ratio differentiated/undifferentiated).
Areas above and below horizontal dashed lines represent increased and reduced interactions
in differentiated cells, respectively (black and white vertical arrows). The genomic
domain is shown to scale above the graphs as in (a). Interaction frequencies represent
the average of at least three PCRs and error bars represent the standard error of
the mean.

To determine whether all or only specific genes interact with each other when repressed,
we mapped the interaction profile of each looping fragment in both cellular states
('Fixed HoxA10, 11, 13' in Figure 3a). Similarly to HoxA9, HoxA10, 11, and 13 interacted frequently with neighboring fragments in undifferentiated and differentiated
cells. Interaction frequency did not rapidly decrease with increasing genomic distance
in undifferentiated cells. In fact, weaker but similar interaction profiles were observed
in both cellular states, which is consistent with the partial gene repression measured
in our samples (Figure 2c). We found that all repressed genes formed strong looping contacts with each other
following differentiation and that silencing was accompanied by overall increased
interaction frequency (Figure 3b). Looping contact intensities were likely underrepresented since HoxA9-13 gene expression was reduced rather than completely silenced in our samples (Figure
2c). Therefore, HoxA gene repression during cellular differentiation involves overall increased chromatin
packaging driven, at least in part, by looping and clustering of co-repressed genes.

Direct quantitative comparison of IFs between cellular states was achieved by measuring
contacts in a gene desert region as previously described (Figure 4) [12]. The gene desert characterized in this study is thought to be transcriptionally silent
and should, therefore, remain unchanged following cellular differentiation. Accordingly,
we found similar chromatin compaction profiles in both cell states where IFs decreased
with increasing genomic distance. This result is consistent with a linear random-coil
chromatin fiber devoid of long-range looping contacts. 3C primer sequences used in
this analysis are presented in Additional data file 2.

Figure 4. The chromatin compaction of a gene desert control region does not significantly change
during cellular differentiation. The y-axis indicates interaction frequency and the
x-axis shows genomic distance between interacting fragments. The average log ratio
of corresponding contacts in undifferentiated and differentiated cells from this dataset
was used to normalize the HoxA 3C datasets shown in Figure 3a. Interaction frequencies represent the average of at
least three PCRs and error bars represent the standard error of the mean.

Together, these results demonstrate that the spatial chromatin organization of the
HoxA cluster is dynamic and depends upon transcription activity. Low-resolution in situ hybridization analysis of the HoxB and D clusters during mouse embryonic stem cell differentiation previously demonstrated
that temporal Hox induction is accompanied by changes in spatial chromatin architecture [40-42]. For example, retinoic acid HoxB gene induction was shown to induce global decondensation and physical exclusion of
the cluster from its chromosome territory. This 'looping out' mechanism was conserved
in the HoxD cluster, suggesting that similar chromatin remodeling mechanisms regulate different
Hox clusters. Interestingly, the Drosophila homeotic bithorax complex was recently found to be organized into higher-order chromosome
structures mediated by the polycomb response elements [43]. In our preliminary 3C analysis we demonstrate that the corresponding human HoxA genes are also organized into looping contacts when transcriptionally repressed. These
results strongly suggest that an evolutionarily conserved structural mechanism regulates
the expression of Hox genes. Comprehensive mapping of the gene clusters will be required both to define
the mechanism(s) regulating Hox expression and identify conserved Hox CCSs of cellular
differentiation.

We characterized 3C libraries with 5C technology to generate high-resolution maps
of the entire HoxA cluster and control gene desert region during THP-1 differentiation. 5C analysis has
been hampered by the lack of publicly available research tools. For this reason, we
developed several computer programs to assist in experimental design, data analysis
and result interpretation. First, we generated '5CPrimer' to design forward and reverse
5C primers directly from any given genomic domain. This program selects primers based
on sequence complexity, length, and melting temperatures, and excludes sequences homologous
to DNA repeats. This program is extensively described in the Materials and methods
and an example of 5CPrimer output is presented in Additional data file 3.

We used 5CPrimer to design the HoxA and gene desert oligonucleotides used in this study (Additional data file 3). 5C libraries
were generated with 58 5C primers using the cellular and control 3C libraries characterized
above as templates (Figure S1a in Additional data file 4). Libraries were produced
with alternating forward and reverse primers corresponding to consecutive restriction
fragments along each region, and contained up to 841 different contacts. These contacts
include 441 interactions within the HoxA cluster, 64 in the gene desert region, and 336 inter-chromosomal genomic contacts.
This experimental design yields the maximum interaction coverage achievable per 5C
library (50%), and generates a matrix of interactions throughout both genomic domains.
To verify that multiplexed 5C libraries contained quantitative 3C contact 'carbon
copies', we measured the levels of four 5C products regulated during THP-1 differentiation
(Figure S1b, c in Additional data file 4; Figure 3a, b). 5C ligation products were measured individually with internal primers as previously
described [16]. We found that 5C libraries closely recapitulated the 3C interaction profiles in
both cellular states, indicating quantitative detection of chromatin contacts in our
5C libraries. 5C internal primer sequences are shown in Additional data file 5.

We analyzed the 5C libraries generated above using custom microarrays. To facilitate
5C array design, we developed the '5CArray' program. This program uses output files
of the 5CPrimer algorithm and can design custom 5C arrays from any genomic region.
A detailed description of this program is presented in Materials and methods. We used
5CArray to design the custom 5C microarrays used in this study. 5C libraries were
hybridized onto arrays as described previously, and normalized IFs were calculated
with the 'IF Calculator' program. We developed IF Calculator to automate IF calculation
and exclusion of signals close to background (see Materials and methods). We first
verified that 5C array results recapitulate 3C analysis by comparing the 3C and 5C
chromatin interaction profiles of four different cluster regions regulated during
THP-1 differentiation (Additional data file 6). We found that 5C array results recapitulated
the overall interaction profiles generated by conventional 3C. However, some variations
were observed, which may be explained by differences in the dynamic range of each
approach as previously reported [16].

To help visualize spatial chromatin architecture changes between cellular states,
we represented the complete HoxA 5C interaction maps as two-dimensional heat maps where the color of each square is
a measure of pair-wise IFs (Figure 5 & Figure 6). Several changes can be observed from these maps. First, THP-1 differentiation is
associated with overall increased chromatin packaging (compare overall IFs from each
map). Second, gain of contacts throughout the cluster in differentiated cells is accompanied
by decreased IFs between neighbors (compare IFs along diagonals in each map). This
result is consistent with the formation of looping interactions and with a linear
detection of DNA contacts in our experimental system. Third, the 3' end of the cluster
(fragments 47-50) interacts very strongly with the entire HoxA region in both samples, suggesting that this region might be located at the center
of the model. Fourth, chromatin remodeling mostly involved the 3' end (fragments 47-50)
and the transcriptionally regulated 5' end (fragments 71-75) of the cluster.

Figure 5. 5C array analysis of chromatin conformation changes in the HoxA cluster during cellular differentiation. HoxA chromatin contacts in undifferentiated cells are presented as a two-dimensional heat
map. Pair-wise interaction frequencies between restriction fragments were detected
by 5C and measured on custom microarrays. A linear diagram of the HoxA gene cluster is presented at the top and right borders and is as described in Figure
2b. A predicted BglII restriction pattern is illustrated below the HoxA diagram and is to scale. Restriction fragments were identified from left to right
by the numbers indicated below each line. Intersecting column and row numbers identify
DNA contact. Values within each square represent interaction frequencies and are color-coded.
The color scale is shown in the bottom left inserts, with pale yellow to brown indicating
very weak to strongest contacts. Interaction frequencies are the average of at least
three array technical repeats. Note: primer 48 was included during large-scale 5C
library production but was excluded from our analysis because of homology to repetitive
sequences.

Figure 6. 5C array analysis of chromatin conformation changes in the HoxA cluster during cellular differentiation. HoxA chromatin contacts in differentiated cells are presented as a two-dimensional heat
map as described in Figure 5.

To identify the most regulated chromatin contacts, we then compared the individual
interaction profiles of each restriction fragment in both cell states (Figure 7a). We found that interaction between the 3' end and the entire HoxA cluster greatly increased following differentiation (Fixed 47 in Figure 7a). We also found that the transcriptionally regulated region interacted more frequently
throughout the cluster in differentiated cells (Fixed 71, 73, 75 in Figure 7a). Interestingly, fragments containing the HoxA1 and A2 genes interacted more frequently with this region after differentiation (Fixed 51,
53 in Figure 7a; green highlight). These results suggest that transcription repression of 5' end
genes induces formation of long-range DNA contacts between the ends of the cluster.
Because the maximum interaction coverage achievable per 5C library is 50%, looping
contacts were not well defined in this experiment (compare Figures 7a and 3a). However, higher resolution can be obtained by combining complementary 5C datasets
or by performing 5C on 3C libraries generated with frequent cutters (for example,
DpnII).

Figure 7. Extensive HoxA spatial chromatin remodeling during cellular differentiation involves the transcriptionally
regulated 5' end region. (a) 5C chromatin interaction profiles with the greatest differences between undifferentiated
and differentiated states were extracted from 5C datasets. The normalized interaction
frequency is plotted logarithmically on the y-axis to emphasize differences between
cellular states. The x-axis shows genomic position relative to the start of the domain
analyzed. The linear HoxA cluster diagram and predicted BglII restriction pattern are shown to scale above the graphs, and are as described in
Figures 2b, 5 & 6. Solid orange vertical lines identify the position of 'fixed' 5C
interaction profiles presented in each graph. Shaded green vertical lines highlight
position of putative 3'-5' looping regions. Each data point is the average of at least
three array interaction frequencies. Error bars represent the standard error of the
mean. (b) 5C chromatin compaction of a gene desert control region does not change during differentiation.
The y-axis indicates interaction frequency and the x-axis shows genomic distance between
interacting fragments. The average log ratio of corresponding contacts in undifferentiated
and differentiated cells from this dataset was used to normalize HoxA 5C datasets shown in Figures 5 & 6 and in (a). Interaction frequencies represent the
average of at least three array interaction frequencies and error bars represent the
standard error of the mean.

In this experiment, we also used the control gene desert region to normalize IFs between
datasets and to determine whether extensive chromatin remodeling was specific to transcriptionally
regulated domains (Figure 7b). As observed by 3C, similar chromatin compaction profiles were found in both cell
states. IFs rapidly decreased with increasing genomic distance, which is consistent
with a linear chromatin fiber devoid of long-range looping contacts. These results
suggest that extensive chromatin remodeling occurs preferentially in transcriptionally
regulated regions during cellular differentiation. Therefore, CCSs might be valuable
predictive signatures of gene expression and may represent an entirely novel class
of human disease biomarker.

Computer modeling of HoxA spatial chromatin architecture

Two-dimensional analysis of 5C interaction maps identified several HoxA chromatin contacts regulated during differentiation. However, this preliminary analysis
revealed an important feature of 5C detection of chromatin remodeling in that regulation
involves both gain and loss of contacts throughout regulated domains (compare Figure
5 and Figure 6). Because two-dimensional data analysis mainly identifies prominent changes in DNA
contacts, this approach does not fully integrate spatial chromatin regulation and
information is lost. For this reason, we developed the '5C3D' modeling program, which
uses the 5C datasets to generate a representation of the average three-dimensional
conformation based on IFs. 5C3D posits that relative IFs are inversely proportional
to the physical distance between DNA segments in vivo. Starting from a random three-dimensional structure, 5C3D moves points iteratively
to improve the fit to the physical distances estimated from the IFs (see Materials
and methods for details). No model was found to match exactly all pairwise distances,
although the deviations were small for all pairs of points. This result is likely
due to IF variability that may originate from experimental error, very low or high
signals, or from experimental design. For example, 5C datasets generated from cell
populations contain averaged IFs derived from various cell cycle states, which can
introduce noise in models. For these reasons, 5C3D generates averaged structural models
rather then true individual in vivo structures. Nevertheless, the model generated by this modeling program, while not
providing a 'true' structure for the chromosome's conformation, still represents a
valuable CCS identification tool.

We used 5C3D to predict three-dimensional models of the HoxA cluster in undifferentiated and differentiated cells (Figure 8a, b). In these models, the overall spatial chromatin density of the HoxA cluster increased following differentiation. This result is consistent with increased
IFs observed in 5C datasets and, importantly, correlates with transcription repression
of 5' end genes. For example, we found that transcriptionally silent 3' end HoxA genes (A1-5) were spatially clustered in undifferentiated cells and that this organization did
not significantly change following differentiation. However, the position of transcriptionally
regulated genes was significantly altered between cell states. In undifferentiated
cells, HoxA9, 11 and 13 are expressed and looped away from the cluster. In contrast, these genes were pulled
back towards the cluster following transcription repression in differentiated cells.
The relative position of HoxA10 did not significantly change following differentiation where, accordingly, it remained
the most highly expressed 5' end gene (Figure 2c). We also found that the position of a region containing HoxA6 was significantly altered following differentiation. Since this gene is transcriptionally
silent in both conditions, this result suggests that physical exclusion of genes from
the cluster is not sufficient for transcription induction.

Visual identification of chromatin conformation changes from three-dimensional models
can be challenging particularly when 5C3D outputs are sensitive to noise in IFs. To
help robustly identify differences between models, we developed the 'Microcosm' program.
Microcosm uses 5C datasets to calculate local chromatin densities within any given
genomic environment, which are then represented graphically. This program minimizes
error from model variability and statistically interprets differences by using multiple
predicted conformations based on a set of pair-specific models of noise in IFs (see
Materials and methods for details). Although Microcosm measures only density and not
identity of surrounding DNA, this program is nonetheless useful to visualize conformational
changes as manageable two-dimensional 'molecular imprints'.

We used Microcosm to estimate local chromatin densities around HoxA genes in both cellular states (Figure 8c). We found that transcriptionally silent 3' end HoxA genes (A1-5) reside in comparable local density environments (see Additional data file 7 for
calculated p-values). These environments did not change significantly following differentiation,
which is consistent with the predicted 5C3D models (Figure 8a, b). In contrast, local densities around HoxA9, 11, and 13 increased significantly upon transcription repression to levels approaching those
of the silent 3' end HoxA genes. Also consistent with predicted 5C3D models, the local density of HoxA10 was comparable in both cell states, whereas the environment of transcriptionally silent
HoxA6 dramatically changed following differentiation. The reason for chromatin remodeling
at the transcriptionally silent HoxA6 gene region remains unknown. However, its position between transcriptionally silent
and regulated domains might identify it as a molecular hinge during formation of contacts
between the ends of the cluster following cellular differentiation.

Nothing is known about the mechanisms involved in the establishment and/or maintenance
of HoxA DNA contacts during differentiation. However, the CAGE (cap analysis of gene expression)
and chromatin immunoprecipitation (ChIP)-chip datasets generated by Suzuki et al. under both cellular conditions correlated well with our findings [3]. For example, CAGE, which quantitatively identifies transcription start sites at
high resolution, specifically detected transcription start sites upstream of the HoxA9, 10, 11 and 13 genes in undifferentiated cells. Consistent with our results, these transcription
start sites were significantly repressed following differentiation. Moreover, transcription
repression of 5' end genes was specifically correlated with reduced acetylated histone
(H3K9Ac) and RNA polymerase II association, which are two markers of active transcription.
Complete mapping of chromatin modifications in the cluster should help understand
the role of DNA contacts in HoxA gene regulation throughout cellular differentiation and in human leukemia cells.

Comparison to similar software

We developed a suite of publicly available 5C computer programs to promote mapping
of functional interaction networks in any non-specialized molecular biology laboratory.
No software similar to '5CArray', 'IF Calculator', '5C3D', or 'Microcosm' existed
prior to this study. A rudimentary program used to predict 5C primer sequences was
previously developed in collaboration with NimbleGen Systems Inc. [16] but was not usable by non-specialists. The original script was written in Perl, was
command line only, and required the installation of several additional packages to
function. The '5CPrimer' computer program presented in this study was written in C
as a command line tool, but a web interface was created for easy access and use of
all features for users of all abilities. 5CPrimer does not require additional packages
to work, but is designed to make use of the RepeatMasker, if installed, to eliminate
repetitive sequences that can potentially cause problems. The output files from the
5CPrimer program are used as the input for the 5CArray program.

Conclusions

In this study, we identified CCSs associated with transcription networks of cellular
differentiation in a human leukemia cell line. The dynamic HoxA CCSs reported here are reminiscent of the three-dimensional structures recently described
in the D. melanogaster homeotic bithorax complex [44]. Therefore, our results suggest that an evolutionarily conserved mechanism based
on chromatin architecture regulates the expression of Hox genes. However, CCS mapping of each Hox cluster in other human differentiation systems will be required to verify evolutionary
conservation of these signatures. The role of chromatin contacts in the regulation
of Hox genes is still unknown and it will be particularly interesting to determine
whether chromatin architecture is required for proper spatio-temporal Hox regulation. Fine mapping of Hox interactions in other cell systems will help identify the DNA sequences and regulatory
proteins mediating both conserved and cluster-specific contacts. In this study, we
also developed valuable tools to identify CCSs of gene expression. These tools will
be useful to identify leukemia HoxA CCSs and to assess the diagnosis and prognosis predictive value of this new type of
signature. Finally, complete mapping of physical interaction networks during differentiation
should help further understand how the underlying transcription network of cellular
differentiation regulates gene expression. This study represents the initial step
towards defining the very first high-resolution molecular picture of a physically
networking genome in vivo during differentiation.

To induce cellular differentiation of THP-1, cells were grown in 225 cm2 flasks to approximately 1 × 105 per 100 ml of complete RPMI. Twelve hours before differentiation, half volume of fresh
media (50 ml) was added to each flask. For differentiation, cells were collected by
centrifugation and resuspended at 2 × 105 per ml in complete RPMI containing 30 ng/ml PMA (Sigma®, St-Louis, MO, USA). THP-1 cells were incubated 96 hours in the presence of PMA or
DMSO (control), and collected for RNA extraction and 3C library preparation.

Real-time PCR quantification

Total THP-1 RNA was extracted from undifferentiated (DMSO control) and differentiated
(PMA) cells with the GenElute™ Mammalian Total RNA Miniprep kit as recommended by
the manufacturer (Sigma®). Reverse transcription was performed with oligo(dT)20 (Invitrogen™) using the Omniscript Transcription kit (Qiagen®, Mississauga, ON, Canada). Gene expression was quantified by real-time PCR with a
LightCycler (Roche, Laval, QC, Canada) in the presence of SYBR Green I stain (Molecular
Probes®, Burlington, ON, Canada). The RT-PCR primer sequences used in this analysis are summarized
in Additional data file 1.

Control 3C libraries

Control 3C libraries are used to correct differences in 3C primer pair efficiency.
A control 3C library for the human Hox clusters was generated from BACs as previously described [12,45]. Briefly, an array of BAC clones covering the four Hox clusters and one gene desert region (ENCODE region ENr313 on chromosome 16) was mixed
at equimolar ratio. Mixed BAC clones were digested with BglII and randomly ligated with T4 DNA ligase. The following BAC clones were used to
generate the library: RP11-1132K14, CTD-2508F13, RP11-657H18, RP11-96B9, RP11-197K24.
BAC clones were obtained from Invitrogen™.

3C analysis

Cellular 3C libraries were generated as previously described [12,45]. Briefly, undifferentiated (DMSO control) and differentiated (PMA) cells were fixed
in the presence of 1% formaldehyde, digested with BglII and ligated under conditions promoting intermolecular ligation of cross-linked
restriction fragments. 3C libraries were titrated by PCR with 3C primers measuring
the IF of neighboring restriction fragments in the control gene desert region described
above (see 'Control 3C libraries'). 3C library quality was verified by measuring the
compaction of the gene desert control region as previously described. HoxA 3C IFs were normalized by calculating the average log ratio of corresponding gene
desert contacts in samples as previously described [12]. PCR conditions were described elsewhere [45]. At least three PCRs were performed for each interaction, and similar results were
obtained from two different sets of 3C libraries. 3C PCR products were resolved on
agarose gels containing 0.5 μg/ml ethidium bromide and visualized by UV transillumination
at 302 nm. Gel documentation and quantification was performed using a ChemiDoc™ XRS
system equipped with a 12-bit digital camera coupled to the Quantity One® computer software (version 4.6.3; BioRad, Mississauga, ON, Canada). 3C primer sequences
are presented in Additional data file 2.

Generation of 5C libraries

Forward and reverse 5C primers were designed with the '5CPrimer' algorithm described
below (see 'Informatics'). Multiplex 5C libraries were produced by mixing 58 alternating
forward and reverse 5C primers corresponding to consecutive BglII fragments in the HoxA cluster and gene desert regions. This 5C experimental design yields 50% interaction
coverage over both genomic regions and measures up to 841 possible contacts simultaneously.

5C library microarray analysis

Multiplex 5C libraries were prepared as described above (see 'Generation of 5C libraries')
and amplified with forward T7 and reverse 5'-Cy3-labeled T3 PCR primers. Custom maskless
arrays (NimbleGen Systems Inc., Madison, WI, USA) were designed with the '5CArray'
computer program described below (see 'Informatics'). Each array featured the sense
strand of all 46,494 possible 5C ligation products within and between the four human
Hox clusters and gene desert region. The array contained several inter-region negative
controls. Each feature was represented by 8 replicates of increasing length ranging
from 30 to 48 nucleotides, which served to identify optimal feature length under our
hybridization conditions. A detailed description of the array design is presented
on our website (see the 'URLs' section below). Maskless array synthesis was carried
out as previously described [46].

Hybridization was carried out with 50 ng of amplified Cy3-5C libraries and using the
NimbleGen CGH Hybridization kit as recommended by the manufacturer and as previously
described [47-49]. Arrays were scanned using a GenePix4000B scanner (Axon Instruments, Molecular Devices
Corp., Sunnyvale, CA, USA) at 5 μm resolution. Data from scanned images were extracted
using NimbleScan 2.4 extraction software (NimbleGen Systems, Inc.).

Informatics

5CPrimer

We developed a program named '5CPrimer' to design forward and reverse 5C primers directly
from a given genomic region. The algorithm first scans a genomic region of interest
supplied in FASTA format to identify the position of restriction sites for any enzyme
selected. 5C primers are then designed iteratively starting from the center of each
cut site. Single nucleotides corresponding to the genomic DNA sequence are added in
a 3' to 5' direction. The melting temperature of the elongating primer is calculated
after each addition using values from nearest-neighbor thermodynamic tables [50]. Nucleotides are added until an ideal melting temperature of 76°C is reached. Because
5C primer sequences are restricted by the position of cut sites, initial primer lengths
are variable and may extend beyond maximum array feature lengths. To harmonize 5C
library and array design, the length of 5C primers was restricted to 72 polymerization
cycles, which corresponds to the optimal number during array synthesis. The number
of polymerization cycles required to generate oligos on arrays is proportional to
complexity, with low complexity oligos requiring more cycles and yielding shorter
feature lengths. 5CPrimer also uses the RepeatMasker software to identify primers
homologous to repeats or low-complexity genomic regions [51-54]. Such primers were previously found to generate false positives, and should be excluded
from experimental designs. Resulting 5C primers contain genomic homology regions ranging
from 19 to 37 bp in length. The 5CPrimer algorithm attaches a modified T7 universal
sequence (TAATACGACTCACTATAGCC) at the 5' end of all forward primers, and a modified
complementary T3 universal sequence (TCCCTTTAGTGAGGGTTAATA) to the 3' end of all reverse
primers. Additionally, all reverse primers are phosphorylated on the 5' end. 5CPrimer
output is a text file, which can be submitted directly for synthesis.

5CArray

We developed a computer program named '5CArray' to design custom 5C microarrays for
any genomic region(s) of interest. This program uses the output from the 5CPrimer
algorithm to determine the sequence of array features, which correspond to any possible
5C products between the forward and reverse 5C primers used in a given study. In addition
to full-length 5C products, the user can specify a range of feature lengths for each
5C product. Varying feature lengths are useful to identify the optimal hybridization
conditions under defined experimental conditions. 5CArray typically designs eight
oligos for each predicted 5C product. Oligo sizes are defined equally from the center
of the reconstituted restriction site and include 30, 36, 38, 40, 42, 44, 46, and
48 nucleotide sequences (combined half-site feature lengths). Oligo sequences only
include complementary genomic regions and always exclude T7 and T3 universal primer
sequences. In cases where one of the 5C primers of the 5C product is short, the program
simply stops adding nucleotides to that end of the oligo. 5CArray outputs each oligo
to a text file with a unique ID code. If arrays are designed from several 5CPrimer
files, the resulting text files need only be merged and can be directly submitted
for array synthesis.

Interaction frequency calculation: the IF Calculator program

5C analysis was conducted with custom arrays featuring half-site probe lengths of
15, 18, 19, 20, 21, 22, 23 and 24 bp as described above (see '5CArray'). The 15-bp
half-site probe signal is representative of background noise and is used to determine
which of the remaining probe values should be included to calculate the average IF
of its corresponding fragment pair. We developed the 'IF Calculator' program to automate
exclusion of points close to background signal. For each interaction and starting
from the longest half-site, IF Calculator first compares the signal of each probe
to the value of the corresponding 15-bp probe. If a signal is found to be less than
150% of the 15-bp values, that half-site signal is discarded along with all remaining
shorter probe length values. Corresponding 15-bp signals are then subtracted from
the remaining values to remove background from each entry. Corrected values are used
to calculate IFs by dividing cellular and BAC 5C signals of corresponding feature
lengths. Interaction frequencies are finally averaged and the variance, count, and
95% confidence interval are reported in the final 5C dataset. If all probe length
values are rejected as background, an IF value of zero is reported and is indicated
as a missing data point.

Three-dimensional model prediction: the 5C3D program

The 5C3D program begins by converting the IFs to distances (D) as follows:

where IF(i, j) is the IF between points i and j and D(i, j) is the three-dimensional
Euclidean distance between points i and j, (1 ≤ i, j ≤ N). Next, the program initializes
a virtual three-dimensional DNA strand represented as a piecewise linear three-dimensional
curve defined on N points distributed randomly in a cube. The program then follows
a gradient descent approach to find the best conformation, aiming to minimize the
misfit between the desired values in the distance matrix D and the actual Euclidean
pairwise distance:

Each point is considered one-at-a-time and is moved in the inverse direction of the
gradient ∇ of the misfit function (for which an analytical function is easily obtained),
using a step size equal to δ*|∇|. Small values of δ (δ = 0.00005 was used) ensure
convergence of the method but increase the number of iterations needed. The process
of iteratively moving each point along the strand in order to decrease the misfit
is repeated until convergence (change in misfit between successive iterations less
than 0.001). The resulting set of points is then considered to be the best fit for
the experimental data and is represented as a piecewise linear three-dimensional curve.

The width of the line is then modified to be proportional to the density of the number
of base pairs in the genome per distance unit. This curve is then annotated with differently
colored transparent spheres centered at the transcription start sites of the genes
present along the DNA sequence. Another option is to surround the strand by identically
colored transparent spheres having their vertices lying on the line to represent the
uncertainty in the exact model of the DNA strand as well as to indicate the density
of the number of base pairs in the genome per distance unit in the virtual representation.

Model comparison: the Microcosm program

In order to compare and find differences between any two models, we developed a program
entitled 'Microcosm'. This program uses two 5C array datasets as input. Datasets feature
the average IF values, variance, counts (or number of technical repeats), and 95%
confidence intervals for each pair of points. To establish the robustness and significance
of the observed structural differences, Microcosm selects an IF at random from the
normal distribution of the corresponding mean and variance. This process is repeated
for each fragment pair to generate 'randomly sampled' 5C array datasets based on original
5C data. Each randomly sampled dataset is then used individually by 5C3D to infer
the best fitting model. The final models are next analyzed to determine the local
density of the environment surrounding each gene G. The local density is defined as the total number of DNA base-pairs from any DNA
segment that lies within the volume of a sphere of a fixed radius centered at G's transcription start site. The process described above is repeated 100 times for
each original 5C dataset to generate 100 individual models and local density estimates
around each gene. The average local density, its variance and 95% confidence interval
for the mean are then calculated for each gene and reported in a graphical format
called a local density plot. Local density plots can be compared to identify genes
with significant differences in local density. A p-value is calculated for each difference and corresponds to the probability of incorrectly
predicting a difference in local densities assuming normality of the data. Small p-values therefore indicate strong degrees of confidence in the difference between
the local densities of a gene's environment between two states. When correlated with
corresponding changes in gene expression, these differences may indicate that transcription
is regulated by changes in chromatin conformation.

Databases

The May 2004 human reference sequence (NCBI Build 35) produced by the International
Human Genome Sequencing Consortium was used for 3C experimental design (see 'URLs'
section below).

URLs

The human genome sequence is available at [55]. Detailed protocols and 3C/5C design support information can be found at [56]. Complete raw datasets and bioinformatics tools developed in this study are also
available at [57]. Tools include '5CPrimer', '5CArray', 'IF Calculator', '5C3D', and 'Microcosm'.

Authors' contributions

JF carried out the 3C and 5C experiments, quantified gene expression by real-time
PCR, and developed the 5CPrimer and 5CArray programs. MR developed the IF Calculator,
5C3D and Microcosm computer programs. SS participated in the 3C and 5C experiments
and the gene expression quantification by real-time PCR. MF designed and validated
the real-time PCR gene expression quantification system and participated in the 3C
experimental design. YH defined the cellular differentiation conditions, provided
the cell system and the initial gene expression data. MB supervised and participated
in the development of all computer programs. JD conceived the study, participated
in its design and coordination, supervised the 3C, 5C and gene expression experiments,
and drafted the manuscript. All authors read and approved the final manuscript.

Additional data files

The following additional data are available with the online version of this paper.
Additional data file 1 is a table listing the human primer sequences for quantitative RT-PCR analysis. Additional
data file 2 is a table listing the human 3C primer sequences used in this study. Additional data
file 3 is a table illustrating the 5C primer sequences generated with the 5Cprimer algorithm.
Additional data file 4 is a figure illustrating quantitative detection of chromatin contacts in our 5C libraries.
Additional data file 5 is a table listing the human internal 5C primer sequences for quality control of
5C libraries. Additional data file 6 is a figure demonstrating that 5C array results recapitulate 3C analysis. Additional
data file 7 is a table listing the p-values of local chromatin densities around HoxA genes shown in Figure 8c.

Additional data file 4.(a) Representative agarose gel resolution of amplified multiplex cellular and BAC 5C libraries.
Libraries were generated by mixing 29 of each forward and reverse 5C primers with
corresponding 3C libraries as described in Materials and methods. BAC 5C library products
typically migrate more heterogeneously then cellular counterparts due to increased
complexity. (b) Linear schematic representation of HoxA cluster region analyzed in (c). Cluster features are as described in Figure 2b. Predicted BglII restriction pattern below HoxA diagram is shown to scale and restriction fragment number is indicated below each
line. Orange shading identifies 'fixed' 3C region and green boxes indicate position
of interacting fragments. (c) Detection of individual 5C contacts in multiplex 5C libraries. Formation of four different
5C ligation products in cellular and BAC multiplex 5C libraries was measured with
internal 5C primers (right). Interaction frequencies in undifferentiated and differentiated
cells were expressed relative to neighboring 71 and 72 interaction, which was set
at one. 5C internal priming results are compared to 3C results shown on the left.
3C data are from Figures 3 &4 except that interaction frequencies were expressed relative to contact neighboring
fixed region as described above. Each histogram value represents the average of at
least three PCR reactions and error bars correspond to standard error of the mean.
5C internal primer sequences are shown in Additional data file 4.

Additional data file 6.(a) Diagram of the HoxA cluster region compared in (b, c). Features are as described in Figure 2b. Predicted BglII restriction pattern below HoxA schematic is shown to scale and restriction fragments are identified below each line.
(b) 3C chromatin interaction profiles from four different fixed cluster regions. Fixed
fragment is indicated above each graph. 3C data are from Figures 3 &4 except that interaction frequency in each cellular state is expressed relative to
contact neighboring fixed region, which was set at one. Each histogram value represents
the average of at least three PCR reactions and error bars correspond to standard
error of the mean. (c) 5C chromatin interaction profiles from four different fixed cluster regions. 5C data
are from Figures 5 &6 except that interaction frequency is presented as described in (b). Interaction frequencies
are the average of at least three array technical repeats. Error bars represent standard
error of the mean.

Acknowledgements

We thank members of our laboratories for stimulating and helpful discussions. We are
grateful to Drs J Teodoro, J Pelletier and H Suzuki for critical reading and comments
on this manuscript. This work was supported by grants from the Canadian Institutes
of Health Research (CIHR) to JD, a Discovery Grant from the National Sciences and
Engineering Research Council of Canada (NSERC) to MB, and research grants for RIKEN
Omics Science Center from MEXT and from the Genome Network Project from the Ministry
of Education, Culture, Sports, Science and Technology, Japan to YH. JF was supported
by funds from the Fonds de la Recherche en Santé du Québec (FRSQ), and MF by a fellowship
from the NCIC. MB is an Alfred P Sloan Fellow. JD is a CIHR New investigator and FRSQ
Research Scholar.